DBMS > Bangdb vs. etcd vs. Microsoft Azure Cosmos DB vs. Realm vs. TinkerGraph

System Properties Comparison Bangdb vs. etcd vs. Microsoft Azure Cosmos DB vs. Realm vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

A distributed reliable key-value store

Globally distributed, horizontally scalable, multi-model database service

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Document store
Graph DBMS
Time Series DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Graph DBMS

Secondary database models

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

bangdb.com

etcd.io
github.com/etcd-io/etcd

azure.microsoft.com/services/cosmos-db

realm.io

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

docs.bangdb.com

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

learn.microsoft.com/azure/cosmos-db

realm.io/docs

Developer

Sachin Sinha, BangDB

Microsoft

Realm, acquired by MongoDB in May 2019

Initial release

2012

2014

2009

Current release

BangDB 2.0, October 2021

3.4, August 2019

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java

Server operating systems

Linux

FreeBSD
Linux
Windows

experimental

hosted

Android
Backend: server-less
iOS
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

JSON types

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

gRPC
JSON over HTTP

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

TinkerPop 3

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
Java

with Android only
Objective-C
React Native
Swift

Groovy
Java

Server-side scripts

Stored procedures

JavaScript

runs within the applications so server-side scripts are unnecessary

Triggers

yes, Notifications (with Streaming only)

yes, watching key changes

JavaScript

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

optional

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

yes

In-Memory realm

yes

User concepts

Access control

yes (enterprise version only)

Access rights can be defined down to the item level

yes

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